VETERBI DECODING METHOD FOR CONVOLUTIONALLY ENCODED SIGNAL

• US 20080152044A1
• Filed: 12/20/2006
• Published: 06/26/2008
• Est. Priority Date: 12/20/2006
• Status: Abandoned Application

• Current Assignee: MediaTek, Inc.
• Original Assignee: MediaTek, Inc.
• Inventors: Chen, Kun-tso
• Application Number:
  US11/613,814
• Publication Number:
  US 20080152044A1
• Time in Patent Office: Days
• US Class Current: 375/341
• CPC Class Codes: H03M 13/41 using the Viterbi algorithm or Viterbi processors
• CPC Class Codes: H04L 1/0054 Maximum-likelihood or sequential decoding, e.g. Viterbi, Fano, ZJ algorithms

Abstract

An improved Viterbi decoding method for convolutionally encoded signals is disclosed. By using the method of the present invention, a trellis of Viterbi Algorithm (VA) for convolutionally encoded SBAS signal, for example, are arranged into butterflies. The butterflies are classified into groups according to branch metrics. Therefore, the butterflies can be effectively processed since the same butterfly processing kernel can be used repeatedly. Therefore, the code size and processing time can be reduced, so that Viterbi Algorithm can be more easily implemented by software.

Claims

1. A Viterbi decoding method for convolutionally encoded signals, all nodes of each stage of a trellis in Viterbi Algorithm used in said method being deduced into butterflies, each butterfly having a specific butterfly ID and including two source nodes and two destination nodes, said method comprising:

receiving symbols composing a data bit;

defining branch metrics for each destination node of each butterfly with the received symbols and branch symbols; and

classifying all the butterflies according to the defined branch metrics.

2. The method of claim 1, further comprising executing a butterfly processing kernel to select survivors for each butterfly by using the defined branch metrics.

3. The method of claim 2, wherein the butterfly processing kernel comprises adding a corresponding one of the defined branch metrics to node metric of each of the source nodes to obtain a destination node metric for the butterfly, comparing the destination node metrics, and selecting the most possible destination node metrics as a survivor.

4. The method of claim 3, further comprising decoding the survivor.

5. The method of claim 2, wherein a butterfly to be processed is selected according to the butterfly ID of the previously processed butterfly and relationship between butterfly IDs and branch metrics.

6. The method of claim 5, wherein the butterfly to be processed is selected farther according to the times that the butterfly processing has been executed.

7. The method of claim 1, wherein the butterflies are classified into groups by butterfly IDs.

8. The method of claim 1, wherein the convolutionally encoded signals comprises SBAS signals.